EEG Data for Patients Receiving Intravenous Antibiotic Medication

- Citation Author(s):
-
Alaa Awad Abdellatif (Qatar University)Zina Chkirbene (Qatar University)Abeer Al-Marridi (Qatar University)Amr Mohamed (Qatar University)Aiman Erbad (Qatar University)Mark Dennis O’Connor ( Hamad Medical Corporation)James Laughton ( Hamad Medical Corporation)Anthony Villacorte ( Hamad Medical Corporation)Johansen Menez ( Hamad Medical Corporation)
- Submitted by:
- Alaa Abdellatif
- Last updated:
- DOI:
- 10.21227/qcg5-yd65
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Abstract
This dataset has been collected in the Patient Recovery Center (a 24-hour, 7-day nurse staffed facility) with medical consultant from the Mobile Healthcare Service of Hamad Medical Corporation. The collection of the raw EEG data is made possible by the use of a non-invasive head-cap type device (i.e., EMOTIV EPOC+ with 14 EEG channels). Using this dataset, we conduct a biological data collection and analysis study for patients undergoing routine planned treatment. The primary objective of our study is the safe collection of EEG data from patients receiving antibiotic therapy, in addition to analyzing the acquired data for detecting the medication side effects, i.e., that might indicate risk of seizure. This study aims to collect and monitor the EEG activity of patients receiving intravenous medication. The acquired EEG data in this study has been collected from 30 patients: before, during, and after receiving the medication. We remark that each column in our dataset files is mapping a particular channel of EMOTIV EPOC+, in addition to a class label column (the first column), i.e., refers to the data collection phase, and a patient identification column (the second column). Number of rows in our dataset represents time instance of the recording channels.
Instructions:
The acquired EEG data has been collected under the Abhath project. The Abhath project is MRC 01-17-091 investigation of the utility of employing techniques of deep learning algorithmic analysis to raw EEG, vital signs and observational data from patients receiving Intravenous antibiotic medication, with respect to using the output data to better predict the risk of seizure events.
Dataset Files
- Patient_1.xlsx (Size: 58.53 MB)
- Patient_2.xlsx (Size: 55.21 MB)
- Patient_4.xlsx (Size: 54.2 MB)
- Patient_5.xlsx (Size: 54.27 MB)
- Patient_6.xlsx (Size: 61.95 MB)
- Patient_7.xlsx (Size: 59.99 MB)
- Patient_8.xlsx (Size: 46.34 MB)
- Patient_9.xlsx (Size: 55.28 MB)
- Patient_10.xlsx (Size: 54.33 MB)
- Patient_11.xlsx (Size: 38.38 MB)
- Patient_12.xlsx (Size: 65.23 MB)
- Patient_13.xlsx (Size: 61.97 MB)
- Patient_14.xlsx (Size: 63.04 MB)
- Patient_15.xlsx (Size: 47.03 MB)
- Patient_16.xlsx (Size: 45.52 MB)
- Patient_17.xlsx (Size: 49.94 MB)
- Patient_18.xlsx (Size: 52.94 MB)
- Patient_19.xlsx (Size: 39.17 MB)
- Patient_20.xlsx (Size: 34.21 MB)
- Patient_21.xlsx (Size: 36.27 MB)
- Patient_22.xlsx (Size: 34.22 MB)
- Patient_23.xlsx (Size: 61.25 MB)
- Patient_24.xlsx (Size: 59.33 MB)
- Patient_25.xlsx (Size: 59.96 MB)
- Patient_26.xlsx (Size: 43.73 MB)
- Patient_27.xlsx (Size: 39.58 MB)
- Patient_28.xlsx (Size: 62.04 MB)
- Patient_29.xlsx (Size: 59.77 MB)
- Patient_30.xlsx (Size: 54.46 MB)
- Patient_3.xlsx (Size: 24.67 MB)
hi
In reply to hi by Ionela Riciu
Hi Lonela
Se pueden conseguir los datos?